Retailers always look for ways to better serve their customers. One of a retailer's the top priorities is to ensure their customers can find the products to fit their wants and needs. In a typical situation, store employees will engage the customer to determine what types of products the customer is looking for. Based on the interaction, the employee can recommend an item that seems to fit the customer's description.
However, customers often are unable to fully articulate the information that the retailer needs to best help the customer. The retailer might not have a large enough employee base to help all the customers in the store. Even if they did, it is likely that the employees will not know every product to the degree needed for the most comprehensive recommendations.
Technological assistance for in-store recommendations is limited. Stores implement loyalty and rewards programs in an effort to track what a customer has purchased. But that technology does not provide real-time product recommendations to a customer when the customer is in the store. Instead, it is used for coupon campaigns and other programs unrelated to in-store engagement.
Meanwhile, Internet technology has developed for web-based product recommendation. This technology usually tracks which products a user has viewed in a browser. Based on that information, recommendations are placed in front of the user as the user browses the web. Shopping web pages can similarly display products based on browsing history.
But this technology does not account for an in-store experience. It does not recommend products the user can buy while they are at the store. And because the technology is largely decoupled from the point of sales, a user is often inundated with advertisements for products they have already purchased or decided not to purchase. As a result, this sort of Internet recommendation technology is not useful for making product recommendations within specific stores. Current technologies do not effectively link the in-store user experience to the online shopping experience.
Most fundamentally, current systems are not capable of recommendations that are unique to the particular store. Unlike a website that is accessed from all over the country or the world, each physical store has specific considerations related to its local customer base. These include product placement and availability, which can be based on geographically-relevant attributes. Current recommendation technology does not address this reality. Instead, product recommendation technology so far has largely remained limited to Internet shopping recommendations.
As a result, a need exists for a store-specific recommendation engine.